As a technique for ascertaining an outline of a large amount of document data, there is a clustering technique of extracting viewpoints included in a plurality of texts and classifying the plurality of texts with respect to each extracted viewpoint.
As such a text clustering technique, for example, NPL 1 discloses a technique of extracting, based on a keyword included in a text, intentions included in a plurality of texts.
In a keyword-based clustering technique, classification is executed based on, for example, a share degree of a keyword among texts. However, in general, in each text to be clustered, a plurality of viewpoints may be mixed. Therefore, even when classification is executed based on a share degree of a keyword, a viewpoint of each cluster may become unclear due to an oversight of a viewpoint, classification of texts having different viewpoints into the same cluster, or the like. In this case, a user is forced, in order to clarify a viewpoint, to perform cumbersome work such that texts of a plurality of clusters are confirmed and the texts are reclassified.
Further, as another technique of text clustering, NPL 2 discloses an entailment clustering technique of extracting an entailment relation between texts and classifying texts having an entailment relation into the same cluster.
The entailment clustering refers to clustering based on an entailment relation that is a relation of meanings between texts. By using entailment clustering, viewpoints included in texts to be analyzed can be extracted without omission, together with representative texts representing an outline of a cluster, and commonly entailed by texts in a cluster.
As a related technique, PTL 1 discloses a technique of generating an entailment graph representing an entailment relation, based on an entailment relation between texts. Further, PTL 2 discloses a technique of viewing and tallying pieces of failure information such as “phenomena,” “causes,” “actions,” and “measures” relating to failures of products.